
As already demonstrated in recent work [1]}, and observed in [2]}, the higher performance obtained in [3]} was due to incorrectly using EEG raw data, because of the drift present in the DC component of the signal that makes block-design classification easier.

However, with properly filtered EEG data, classification accuracy reaches about 50% on 40 classes, which is still significant. More importantly, the obtained performance seems to be supported by cognitive neuroscience studies.

Our counter-analysis, aimed at investigating the presence and influence of temporal correlation on classification performance, reveals that our classification results of the block-design data are unaffected (or only slightly affected). Thus, the data published in [3]} is correct, and importantly, the effect of the temporal correlation claimed in [2]} is marginal.

Our replication of experiments in [2]} with rapid-design data with block-level labels shows
a small contribution of temporal correlation to classification performance (less than 5%, as shown in Table REF ), that is an order of magnitude less than what [2]} reports (about 50%).

The results presented in [2]} appear to be a consequence of their failure to follow standard EEG/ERP study design procedures, particularly those related to the duration of experiments — recommendations that [3]} did follow. In addition, they did not replicate the analysis in [3]}, which pooled data from all subjects.

We verify that the classification results reported in [2]} are similar to what we obtain when we intentionally create temporal correlation in our EEG data. This seems to suggest that [2]}'s data suffers from a strong temporal correlation.

Given that [2]}'s primary evidence for a temporal correlation in [3]} stems from the very similar performance of the block-design dataset and the random-design dataset using block-level labels, and that this similarity exists only for their data and not our data, [2]}'s criticism of the block-design experiment in [3]} is invalid.

In conclusion, our counter-analyses of the experimental settings and results in [2]} suggest a strong temporal correlation in their data due to erroneous experimental procedures and uncovential analysis practices.

